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FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non\u2010IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non\u2010IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density\u2010based clustering and genetic hyperparameter optimization. The results are bench\u2010marked using MNIST handwritten digit dataset and the CIFAR\u201010 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non\u2010IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR\u201010 dataset with only 10 training rounds.<\/jats:p>","DOI":"10.1155\/2021\/7156420","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T06:35:09Z","timestamp":1637303709000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0006-267X","authenticated-orcid":false,"given":"Shaashwat","family":"Agrawal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2030-9438","authenticated-orcid":false,"given":"Sagnik","family":"Sarkar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-3704","authenticated-orcid":false,"given":"Mamoun","family":"Alazab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4209-2495","authenticated-orcid":false,"given":"Praveen Kumar Reddy","family":"Maddikunta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-801X","authenticated-orcid":false,"given":"Thippa Reddy","family":"Gadekallu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-9216","authenticated-orcid":false,"given":"Quoc-Viet","family":"Pham","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"e_1_2_9_2_2","unstructured":"ParimalaM. 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